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Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
Jean-Michel Roué1, Iris Morag2, Wassim M Haddad3
1Neonatal & Pediatric Intensive Care Unit, Brest University Hospital, University of Western Brittany, Brest, France jean-michel.roue@chu-brest.fr.
Insights
Accurately assessing infant pain is challenging. This study explores a new multimodal approach using sensors and machine learning for objective, continuous pain monitoring in newborns and infants.
Area of Science:
- Biomedical Engineering
- Neonatal Medicine
- Pain Management
Background:
- Objective pain assessment in non-verbal infants is difficult, relying on subjective measures.
- Pain in newborns can lead to long-term behavioral and cognitive issues.
- Current pain assessments are labor-intensive and observer-dependent.
Purpose of the Study:
- To develop and evaluate a multimodal pain assessment approach for non-verbal infants.
- To enable objective, patient-centered, and context-dependent pain measurement.
- To potentially allow for continuous pain monitoring in clinical settings.
Main Methods:
- Utilizing facial electromyography, ECG, electrodermal activity, oxygen saturation, and electroencephalography.
- Employing sensor-fusion and machine-learning algorithms for data analysis.
- Conducting a prospective observational study in 60 preterm and term newborns/infants.
Main Results:
- The multimodal approach has the potential to improve pain assessment accuracy.
- This method may offer continuous pain monitoring capabilities.
- Feasibility will be assessed in a study of infants up to 6 months old.
Conclusions:
- A multimodal, sensor-based approach offers a promising avenue for objective infant pain assessment.
- Further validation and refinement are planned to optimize sensor requirements.
- This technology could significantly advance neonatal and infant pain management.
Introduction:
Objective pain assessment in non-verbal populations is clinically challenging due to their inability to express their pain via self-report. Repetitive exposures to acute or prolonged pain lead to clinical instability, with long-term behavioural and cognitive sequelae in newborn infants. Strong analgesics are also associated with medical complications, potential neurotoxicity and altered brain development. Pain scores performed by bedside nurses provide subjective, observer-dependent assessments rather than objective data for infant pain management; the required observations are labour intensive, difficult to perform by a nurse who is concurrently performing the procedure and increase the nursing workload. Multimodal pain assessment, using sensor-fusion and machine-learning algorithms, can provide a patient-centred, context-dependent, observer-independent and objective pain measure.
Methods And Analysis:
In newborns undergoing painful procedures, we use facial electromyography to record facial muscle activity-related infant pain, ECG to examine heart rate (HR) changes and HR variability, electrodermal activity (skin conductance) to measure catecholamine-induced palmar sweating, changes in oxygen saturations and skin perfusion, and electroencephalography using active electrodes to assess brain activity in real time. This multimodal approach has the potential to improve the accuracy of pain assessment in non-verbal infants and may even allow continuous pain monitoring at the bedside. The feasibility of this approach will be evaluated in an observational prospective study of clinically required painful procedures in 60 preterm and term newborns, and infants aged 6 months or less.
Ethics And Dissemination:
The Institutional Review Board of the Stanford University approved the protocol. Study findings will be published in peer-reviewed journals, presented at scientific meetings, taught via webinars, podcasts and video tutorials, and listed on academic/scientific websites. Future studies will validate and refine this approach using the minimum number of sensors required to assess neonatal/infant pain.
Trial Registration Number:
ClinicalTrials.gov Registry (NCT03330496).
